Literature DB >> 36040010

Contribution of behavioural variability to representational drift.

Sadra Sadeh1, Claudia Clopath1.   

Abstract

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. Recent work has suggested a large degree of drift in neural representations even in sensory cortices, which are believed to store stable representations of the external world. While the drift of these representations is mostly characterized in relation to external stimuli, the behavioural state of the animal (for instance, the level of arousal) is also known to strongly modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational changes. We analysed large-scale recording of neural activity from the Allen Brain Observatory, which was used before to document representational drift in the mouse visual cortex. We found that, within these datasets, behavioural variability significantly contributes to representational changes. This effect was broadcasted across various cortical areas in the mouse, including the primary visual cortex, higher order visual areas, and even regions not primarily linked to vision like hippocampus. Our computational modelling suggests that these results are consistent with independent modulation of neural activity by behaviour over slower timescales. Importantly, our analysis suggests that reliable but variable modulation of neural representations by behaviour can be misinterpreted as representational drift if neuronal representations are only characterized in the stimulus space and marginalized over behavioural parameters.
© 2022, Sadeh and Clopath.

Entities:  

Keywords:  arousal; behavioural state; behavioural variability; computational biology; mouse; neuroscience; pupillometry; representational drift; running; systems biology; visual processing

Mesh:

Year:  2022        PMID: 36040010      PMCID: PMC9481246          DOI: 10.7554/eLife.77907

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.713


  43 in total

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Review 5.  Variance and invariance of neuronal long-term representations.

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Journal:  Elife       Date:  2017-12-19       Impact factor: 8.140

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Journal:  Nat Neurosci       Date:  2019-09-24       Impact factor: 24.884

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Authors:  Aman B Saleem; Aslı Ayaz; Kathryn J Jeffery; Kenneth D Harris; Matteo Carandini
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9.  Population code in mouse V1 facilitates readout of natural scenes through increased sparseness.

Authors:  Emmanouil Froudarakis; Philipp Berens; Alexander S Ecker; R James Cotton; Fabian H Sinz; Dimitri Yatsenko; Peter Saggau; Matthias Bethge; Andreas S Tolias
Journal:  Nat Neurosci       Date:  2014-04-20       Impact factor: 24.884

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